1. Field of the Invention
The present invention relates to an anti-jammer pre-processor used in a wireless communication receiver, and more specifically, to a blind anti-jammer pre-processor for a Global Positioning System receiver.
2. Description of the Related Art
Global positioning system (GPS) employs satellite-based location techniques to provide precision navigational capabilities for aircraft, ships, and ground traffic. GPS receivers are effective in extracting the navigational information signals transmitted from satellites due to a large processing gain with spread spectrum techniques. Nevertheless, performance will significantly degrade if any strong interference source coexists with the information signal. The interfering signal is frequently referred as a jammer. Typically, a jamming power level less than 40 dB with respect to the signal power level, i.e. jammer-to-signal ratio (JSR) of 40 dB, can be tolerated. In practice, this is impossible since the existence of strong unintentional radio frequency interference and intentional jammers are easily generated due to weakness of the received GPS satellite signal of about −160 dBm on the ground, will disable the receiver. Consequently, a technique for powerful jammer suppression has gained much attention.
A beamformer is a linear combiner which transforms the array data vector x into a scalar output y via an M×1 complex weight vector w, where M denotes the number of antennas:y=wHx  Equation (1) 
The design of a beamformer for the above scenario involves minimizing the output power subject to a unit response constraint in the composite steering vector associated with the Signal-of-Interest (SOI) ad. Specifically, it determines the optimum weight vector w by solving the following optimization problem:                                                         min              w                        ⁢                                                   ⁢                          E              ⁢                              {                                                                          y                                                        2                                }                                              ≡                                    w              H                        ⁢            Rw                          ⁢                                  ⁢                              subject            ⁢                                                   ⁢            to            ⁢                          :                        ⁢                                                   ⁢                          w              H                        ⁢                          a              d                                =          1                                    Equation        ⁢                                   ⁢                  (          2          )                                    Where R is the M×M data correlation matrix defined byR=E{xxH}=σd2αdαdH+AMSMAMH+σn2I ≈AMSMAMH+σn2I  Equation (3)         where the K×K matrix SM=E{sMsMH} denotes the source correlation matrix involving the interferers. The noise correlation matrix is given by σn2I due to the spatial whiteness assumption. Note that the approximation in Equation (3) follows from the fact that the desired signal is well below the powers of interferers, i.e., σn2<<σi2, where σi2 denote the powers of interferers. In practice, the data correlation matrix in Equation (3) is replaced with the time-averaging operator given by:                               R          ^                =                              1            N                    ⁢                                    ∑                              n                =                1                            N                        ⁢                                                   ⁢                                          x                ⁡                                  [                  n                  ]                                            ⁢                                                x                  H                                ⁡                                  [                  n                  ]                                                                                        Equation        ⁢                                   ⁢                  (          4          )                            where x[n] denotes the nth sample of the received data and N is the number of samples.        
The Linear Constrained Minimum Variance (LCMV) problem in Equation (2) has the solution:w=R−1αd  Equation (5) 
Note that the constant gain in w has been omitted since it does not affect the output SINR performance. A major problem of the direct implementation of the LCMV beamforming is that the composite steering vector ad is not practically possibly.
Another widely used approach is to obtain the signal weights to produce the Minimum Mean Square Error (MMSE) between the desired temporal signal sequence Sd and the output of beamformer y. That is, the weights are constructed according to minimizing Equation (6),                                                         ɛ              =                            ⁢                                                E                  ⁢                                      {                                                                                                                                                S                            d                                                    -                          y                                                                                            2                                        }                                                  =                                  E                  ⁢                                      {                                                                                                                                                S                            d                                                    -                                                                                    w                              H                                                        ⁢                            x                                                                                                                      2                                        }                                                                                                                          =                            ⁢                                                E                  ⁢                                      {                                                                  S                        d                                            ⁢                                              S                        d                        *                                                              }                                                  -                                                      w                    H                                    ⁢                  E                  ⁢                                      {                                          xs                      d                      *                                        }                                                  -                                                      x                    H                                    ⁢                  E                  ⁢                                      {                                          ws                      d                                        }                                                  +                                                      w                    H                                    ⁢                  E                  ⁢                                      {                                                                  x                        *                                            ⁢                                              x                        T                                                              }                                    ⁢                  w                                                                                        Equation        ⁢                                   ⁢                  (          6          )                                    which yields the solution given by:w=R−1ds  Equation (7)         where ds=E{x*Sd}.        